Forward Deployed Engineer

Location: Mumbai, India (in office 5 days a week)

Position Summary

This is a founding engineering role at the center of our client's AI strategy. Our client is a global investment research, analytics, and accounting firm that serves private equity funds, family offices, credit and hedge funds, investment banks, and corporates across North America, Europe, the Middle East, and Asia-Pacific. The role follows the Forward Deployed Engineer model: hands-on technical leaders who embed with the business, own delivery end to end, and take full ownership of the outcomes they ship.

The mandate unfolds in two stages. In the first, you will embed with the firm's internal investment, diligence, portfolio monitoring, and accounting teams to find the highest-value AI and automation opportunities in its own delivery model and ship them into production. The role reports up to the CEO, with access to senior leadership on a daily basis, the right candidate should have excellent communication skills both written and verbal.

In the second, you will take proven capabilities into the portfolio companies of the firm's private equity and family office clients. These are mid-market operating businesses whose finance and operations functions often run on fragmented systems, manual reporting, and limited internal technical resources. Working with portfolio company CFOs, controllers, and operating teams, and alongside the sponsor's operating partners, you will design and deliver solutions that improve reporting quality, operational efficiency, and visibility for both management and ownership.

You will design, build, and deliver full-stack solutions and the AI-ready data foundations beneath them. The goal is to turn manual, spreadsheet-based processes into automated, standardized, and auditable systems that professionals and AI agents can both rely on.

The position calls for someone equally comfortable walking a portfolio company controller through a close process, presenting to a sponsor's operating partner, and building the data model, integration, and application that will transform both.

What Success Looks Like

In the first 12 months, you will have:

- Shipped multiple production AI solutions embedded in the firm's core delivery workflows.
- Established reusable data, integration, and governance patterns that speed up every initiative that follows.
- Led the firm's first portfolio company deployments from scoping through adoption, with measurable improvements in reporting timelines, data quality, or operating efficiency.
- Earned recognition as a trusted technical authority with the firm's senior leadership, its sponsor clients, and portfolio company management teams.

This is a highly visible role with a direct line to senior leadership..

Primary ResponsibilitiesBusiness Discovery & Requirements (≈25%)

- Partner with the firm's VPs, Associates, and Analysts, and with portfolio company finance and operations teams, to understand business needs and document current-state processes, calculation logic, systems, and data sources.
- Work with sponsor operating partners and portfolio company leadership to identify where automation and AI will have the greatest effect on value creation priorities.
- Translate business processes into clear technical requirements and target-state designs.
- Facilitate working sessions and process walkthroughs to validate findings, resolve inconsistencies in how metrics are defined, and align stakeholders on standardized definitions.




- Act as a trusted advisor to management teams and sponsors on what automation can realistically deliver, and in what order.
- Tailor each portfolio company solution to what discovery uncovers, rather than to a predefined catalog of offerings.

Solution Architecture, Design & Delivery (≈50%)

- Architect AI-ready data foundations that unify portfolio company data across ERP and accounting systems, CRM, billing, payroll, inventory, and other operational platforms. Deliver them as clean, governed data models and reporting layers.
- Design and build automation and AI-enabled solutions across the portfolio company finance function.
- Support the integration of add-on acquisitions by consolidating data across disparate systems and standardizing charts of accounts and reporting definitions.
- Build LLM- and agent-based applications where they add value, including document extraction, variance commentary, report drafting, and exception monitoring, using the OpenAI and Anthropic APIs and related tooling.
- Build on the firm's initial platform stack of Microsoft Fabric, Azure, and Power BI, and evaluate alternative platforms and vendor solutions where appropriate.
- Work hands-on to prototype and ship automated replacements for manual processes, starting with the highest-volume, highest-risk workflows.
- Deliver solutions inside portfolio company environments and in line with each company's security, access, and data-handling policies.
- Build evaluation frameworks that measure solution quality against workflow-specific benchmarks, such as close cycle time, reconciliation breaks, reporting accuracy, and reviewer override rates.
- Establish reusable components, integration templates, and deployment playbooks so that each new portfolio company is faster to onboard than the last, and maintain data lineage from source system to final report.

Governance, Enablement & Client Development (≈25%)

- Design human-in-the-loop review workflows, source traceability, audit trails, and exception handling so that every output can withstand management, board, lender, and audit scrutiny.
- Uphold the firm's AI governance framework, including SOC 2 and ISO 27001 aligned controls and the requirement that client data never be used to train third-party models.
- Train portfolio company finance teams and the firm's own professionals in effective and responsible use of the solutions delivered, and support change management as manual processes are retired.
- Take part in meetings with sponsors and portfolio company management in the United States, Europe, and the Middle East, and support demonstrations, proposals, and expansion across a sponsor's portfolio.
- Track and communicate progress to senior leadership and sponsor clients, identify data quality and control gaps, and define what it will take to close them.
- Help shape technical hiring and standards as the practice grows.

RequirementsEducation & Certifications

- Bachelor's degree required.
- A concentration in computer science, engineering, data science, finance, accounting, or a related field preferred.
- A CFA, CA, MBA,



or master's degree in a technical discipline preferred.

Professional Experience

- 4+ years of relevant experience in software, data, or ML engineering, or in finance, consulting, KPO, or investment roles with significant hands-on technical responsibility, required.
- Strong proficiency in Python and SQL, with experience working with messy, real-world financial and operational data, required.
- Experience integrating with or extracting data from ERP and accounting systems, such as NetSuite, Microsoft Dynamics, SAP, Sage Intacct, QuickBooks, or Tally, required.
- Hands-on experience building and deploying applications powered by large language models, including retrieval, structured extraction, or agent-based workflows, required. Production experience preferred.
- Working familiarity with modern AI and data platforms, such as Microsoft Fabric, Azure, Power BI, Databricks, OpenAI and Anthropic APIs, and vector databases, with the ability to evaluate alternatives.
- Working knowledge of corporate finance and accounting workflows, such as month-end close, financial statement preparation, FP&A;, cash flow forecasting, and lender reporting.
- Exposure to private equity-backed or family office-owned companies, value creation initiatives, or post-acquisition integration preferred.
- Experience delivering for international clients from India, and willingness to work with partial overlap with US and European business hours, preferred.
- Familiarity with data governance, auditability, and confidentiality requirements in high-stakes environments.

Competencies & Attributes

- Builder's instinct under ambiguity. You measure progress in working software, not slides, and you can turn a vague business problem into a working prototype within weeks.
- Operator empathy. You understand that portfolio company finance teams are stretched thin. You design solutions that reduce their workload rather than add to it, and you earn adoption by sitting with users.
- Platform mindset. You treat every portfolio company as a chance to make the next one faster. You build patterns, not one-off solutions.
- Executive presence. You can sit across from a portfolio company CFO or a sponsor's operating partner, ask the right questions, push back when necessary, and earn trust.
- Intellectual honesty about AI. You know what current models can and cannot do, you design around their limitations, and you do not confuse a compelling demonstration with production reliability.
- Commitment to confidentiality. You treat client data security and governance as non-negotiable.
- Ownership and autonomy. You are comfortable acting as the first technical hire, setting direction, and being accountable for outcomes.

What We Offer

- Founding ownership. You will be the first technical hire for a new practice, with direct exposure to senior leadership and the autonomy to set technical direction from day one.
- Global client exposure. You will work directly with international sponsors, family offices, and portfolio company management teams across the US, Europe, and the Middle East.
- A transparent growth path. This role is designed to grow into leadership of the firm's AI engineering practice as it scales.
- Investment in your development. Structured training, mentorship from leaders with bulge-bracket and institutional experience, and support for relevant certifications.
- Comprehensive health coverage for you and your dependents.
- Competitive Compensation package.

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